Epidemiological studies, clinical trials of new drugs, and clinical monitoring of subjects with organ specific pathology which can be reflective of systemic disease depend greatly on precise measurements and characterization of the pathological lesions as well as anatomical or morphological features in the organ that are indicators of the health of the organ and system. For example, retinal diseases such as Age Related Macular Degeneration (ARMD), Diabetic retinopathy DR), Glaucoma and some systemic diseases having effects upon the eye, such as hyperlipidemia, arthrosclerosis, cylomegalovirus, cancer and hypertension for example depend greatly on precise measurements and characterization of pathological lesions as well as anatomical or morphological features in the eye. To date, quantization of the state of the disease as expressed by photographs or digital images has relied on the tedious task of an individual (grader) manually grading the images, for example retinal images, usually using 35 mm film, stereo viewers and light boxes.
For example, ARMD is the leading cause of irreversible visual loss among the elderly in the US and Europe. Epidemiological studies of ARMD vary in their outcomes using population-based prevalence studies. One of the reasons for such variance is the predominance in the use of qualitative methods to detect and characterize the lesions, for example drusen and choroidal neovascularization (CNV) associated with ARMD. Drusen are lipid deposits that occur in one of the retinal layers and present as light colored lesions in retinal photographs. Drusen represent an example of a type of biomarker that is indicative of disease in the eye. CNV is the more severe stage of the disease where the integrity of the blood vessels in the choroid has been compromised.
Although standardized protocols for detecting and classifying ARMD have been developed, these protocols depend extensively on highly trained “graders” to characterize morphological features (alternatively referred to as descriptors or aspects) of these lesions (alternatively referred to as “features of interest”). Features of interest can be described in terms of their shape, size, edges and color or any combination thereof, which are referred to as characteristics or feature characteristics. Consequently, there is a high degree of subjectivity, leading to inconsistencies in inter- and intra-grader comparisons, which leads to increased errors or uncertainty in the analysis data.
For example, when diagnosing and managing ARMD, graders will manually estimate the numbers, sizes, locations, and spatial extent of lesions at each point in the subject's history based on a rigorous protocol.
A similar situation is present when attempting to quantitate the state of retinopathy in the retina caused by diabetes. Graders qualitatively assess the image to be analyzed by visually comparing it to a standard. There is no explicit attempt to quantitate the disease, for example, by counting the number of micro-aneurysms (MA), or measuring the area of neovascularization on the retina.
Because of time constraints imposed on the graders and the human's limited visual perception capabilities, the grader's accuracy for comprehensively counting and estimating size and area are significantly affected for subjects with a large number of lesions. Similarly, in clinical trials, the precision in measuring the changes in lesions will impact the statistical analysis for assessing a drug's efficacy and/or safety. In the clinic, determining the rate of a disease's progression over time requires exact measures of the lesion or retinal feature being monitored during longitudinal studies.
Interest in automating the analysis and diagnosis of medical or bio-images has been reflected in the increasing number of publications addressing the need and describing the shortcomings of the attempts at segmentation, registration, and computer-aided diagnosis. For example, currently, the effort required by an expert retinal grader or clinical technician to precisely, accurately, and comprehensively quantitate all the pathological characteristics, morphological features or biomarkers in the eye that are indicative of eye disease and the health of the system is prohibitive. Typically, based on the grader's judgment, only the most critically significant or most salient lesions are analyzed.